Introduction

Because the data extracted and presented for pattern recognition or classification in medical images are often complex, noisy, or incomplete, it is difficult to use conventional algorithmic approaches to determine the truth based on a set of predefined rules. As a result, many machine learning methods have been tested and used in the field of medical image processing where computer-assisted diagnosis or classification is involved. Among the variety of machine learning techniques, artificial neural networks (ANNs) and Bayesian belief networks (BBNs) are popular choices because of the inherent function approximation and decision-making capability of these two networks. In this chapter, we first briefly introduce and compare the basic characteristics of ANNs and BBNs used in medical image processing. In order for a computer-assisted diagnosis system involving either an ANN or a BBN to achieve good and robust testing performance in the clinical environment, an unbiased database and a set of effective features will be important considerations in the development phase of the system. Thus, we will focus the discussion on the issues and methods related to optimization of database and feature selection used in ANNs and BBNs for medical image processing.